Adelphic Is Proud to Announce a New Approach to Cross-Channel Advertising

August 17, 2016 in Blog

Adelphic’s VP of Product, Yael Avidan, provides an overview of Adelphic’s new cross-channel solution. 

Understanding consumers’ device usage is beneficial, but understanding consumers’ behaviors across multiple channels, in addition to the devices used, provides much richer data to power campaigns. This is what comprises our proprietary data set, which we refer to as our Behavior Graph™–which powers real-time optimization based on a complete, cross-channel user profile.

Having the Behavior Graph™ integrated into Adelphic’s decisioning layer enables marketers to transition to a real user-centric approach to campaign planning, management, optimization and reporting–and drive superior performance and efficiencies for campaigns targeting consumers on the move.

Cross-channel sits at the heart of Adelphic’s platform.



Adelphic’s cross-channel solution is comprised of:

  • Adelphic’s Device Graph:  Adelphic’s proprietary device graph leverages non-PII behavioral signals in addition to contextual and location signals in order to identify and link devices.  The device graph includes intra-device as well as cross-device, cross-channel links and makes use of deterministic data when possible, but scales using a probabilistic algorithm.   Over trillion data points serve as starting point and then filtered.  Between 50-100 features are used to identify user pairs. The graph is based on roughly 40% in-app requests and 60% web requests and is continuously updated based on real-time data collection.   Adelphic holds a patent for audience recognition across multiple devices.
  • Adelphic’s Behavior Graph™: Behavioral signals, captured in real-time, are mapped back to the device graph to form a richer dataset that joins identity and behavior.  Behavioral signals range from performance (e.g. clicks and conversions) to behavior patterns (e.g. time of day, location, browsing behavior).
  • Adelphic’s  ^tag (“a-tag”):  A persistent identifier that overcomes the lack of a standard user identifier for mobile and desktop (and future channels).  Using Adelphic’s Behavior Graph™,  multiple devices and their associated behaviors can be assigned to single ^tag, creating user profiles that incorporate data from multiple channels
  • Adelphic’s Predictive Performance Engine:  User behavior and ad performance history across devices and channels are used in real-time to predict the value of a user for a specific ad  (e.g. did this user see this ad on a different channel? Does this user convert more on mobile or desktop?).  The engine leverages cross-device user models as well as contextual models (combined via a combination model) to drive bidding decisions and superior performance.

Our solution enables flexible onboarding of audience segments (1st party, 3rd party, campaign data, pixel-based), forecasting available inventory pools and targeting them across channels.  Adelphic’s Predictive Performance Engine sits at the heart of the solution by leveraging users’ complete profile to drive performance and efficiency.   

Key strategies that are supported via Adelphic’s solution:

  • Cross channel targeting across Display, Mobile,  Video and cTV.
  • Extending the reach of advertiser’s retargeting audiences
  • Extending the reach of lookalike segments
  • Extending the reach of behavior-based segments
  • Creative sequencing across multiple channels
  • Frequency management across channels 

More information:

  • How Cross-device identity matching works – Here
  • Looking for a Cross-Device Solution? 3 Questions Ad Buyers Should Ask – Here
  • Adelphic Launches First Behavior-Centric Cross-Channel Programmatic Ad Solution – Here

Central to our cross-channel solution is, of course, privacy.  Adelphic is fiercely focused on consumer privacy and takes specific steps to ensure that our user profiles are anonymous. Neither we nor the clients who partner with us can glean personally identifiable information from the consumers we engage, even by accident. Adelphic is a member of the Digital Advertising Alliance and complies fully with the DAA’s Self-Regulatory Principles for Online Behavioral Advertising and Self-Regulatory Principles to the Mobile Environment. Adelphic also allows opt out from behavioral tracking through Ghostery.

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Looking for a Cross-Device Solution? 3 Questions Ad Buyers Should Ask

August 17, 2016 in Blog

Adelphic’s Director of Product, Dr. Justin Pniower, lends his perspective on 3 questions media buyers should ask about cross-device advertising.  

Ad buyers have grown accustomed to siloed campaign strategies – separate budgets for mobile, desktop and television. But an audience-based approach executed across the devices of a consumer can be a powerful tool, allowing you to interact with and follow users as they move between devices and channels. But it can be hard, even for experts, to evaluate the quality and scale of these solutions, and industry standards do not yet exist. To determine whether the promise of cross-device solutions can deliver, here are some questions you need to ask.

How is quality measured?

Precision, the percentage of links that are correct with respect to a test dataset, is typically the key metric for assessing the quality of a cross-device solution. But, as is the case with other forms of measurement, values can differ substantially based on the details, like what counts as a correct or incorrect link with respect to the test dataset, or how a link is counted if one device is outside the test dataset.

The test dataset itself also has an effect on the precision measurement. You should inquire about the source and quality of the test data, but you should also inquire about the size of the dataset. By shrinking the size of the test dataset, vendors can inflate precision measurements.

How many connections?

When it comes to cross-device measurement, precision does not indicate anything about scale.  You can have perfect precision with just one link. Generally people consider the central metric for scale to be recall, the percentage of links in the test dataset that the solution was able to identify. But you can still have good recall without good scale, and vice versa. For that reason, you also want to know the total number of connections associated with a given level of precision in order to get a better sense of scale.

But the number of connections alone won’t provide all of the information you need. It’s important to understand the number of each type of connection – intra-device, cross-device and cross-channel – as well.

Not all connections are between different devices. While “device graph” is the common term for a map that links individual consumers to each of their own devices, it’s actually a bit of a misnomer. At the most granular level, the nodes on the device graph are unique device identifiers, not unique devices, and there can be multiple identifiers for a single device. When vendors characterize the size of their graphs, they may be sharing the number of links between device identifiers (intra-device), not the number of links between devices (cross-device). When it comes to your return on ad spend, the difference is important.

A user’s browsing and location history can be very different on a mobile device and a desktop making these links, across channels, among the hardest to establish. In order to identify a cross-channel connection, a robust dataset for each channel is required. As such, the majority of links within a graph are likely between devices in the same channel. Inquire about the percentage of connections that are truly cross-channel as well as their relative precision.

What is the effect on my data and KPIs?

While the counts are important indicators, they do not guarantee scale for your data. First-party data is the ad-buyer’s “holy grail,” so it’s important to know the match rate for identifiers you want to extend using the graph in order to truly optimize your ad spend – and your campaign – across specific consumers’ devices.

But if a cross-device solution cannot improve your KPIs, then other metrics do not matter. Performance KPIs like awareness, engagement and conversion rates work well to measure a cross-device campaign. Solutions that enable sequencing and frequency capping across devices can help bolster these KPIs and reduce a consumer’s urge to activate an ad blocker.

While cross-device technology has been around for a while, we’ve only just begun to activate it. As the number of screens in front of consumers continues to grow, the industry must work quickly to develop standards that empower media buyers to evaluate and leverage this technology to shift to a true audience-based approach. In the meantime , it’s important to be educated on the topic in order to make the best possible buying decisions.

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How Cross-Device Identity Matching Works (part 1)

August 12, 2016 in Blog

Martin Kihn at Gartner provides a great primer and explanation of Adelphic’s patented methodology for cross-device identity matching.

Cross-device identity is the thing that tells marketers and other nosy types that Device A and Device B and Browser C all belong to the same Person X. Devices (and browsers, which we’ll call devices for convenience) are matched to people. These people may be known to us by name or they may remain quite mysterious. But here we are concerned only with the process of matching devices together that all point to someone.

How is this done?

Talking to our friends at Adelphic recently, they clued us in to their U.S. Patent 8,438,184 B1. Adelphic is a mobile demand side platform, in the business of helping advertisers target people mainly on mobile devices, so they’re interested in identity. Why? If they’ve served an ad to Person X on Device A, they’d like to know that fact before they bid good money on yet another ad to the same person on Device B. Or if they happen to know something interesting about Person X (say, they like Bernese mountain dogs), then when that person shows up on a new Device C, Adelphic can put in a high bid for their Bernese mountain dog tea cozy-selling client.

Boom! But only – of course – if they know that Devices A, B and C all belong to the same Bernese mountain dog lover Marty, I mean, Person X.

How? That’s the topic of the patent, which I have read in some detail. (Patent reading is about as fun as going to Coney Island to count the sand, so you’re welcome.) I summarize highlights for the people.


It turns out that the “matcher” (e.g., Adelphic) is in the business of building up a large database of individuals. Each individual has a unique identifer (we can call it the Master ID) and a set of known Attributes. These Attributes are things that the database knows about them based on previous interactions. And – importantly – this Master ID and set of Attributes also tries to include other ID’s that are uniquely tied to particular devices.

So at its core, the matcher is a system that is confronted with device identifiers that it bumps up against its Master ID database over and over in an attempt to see if that device belongs with an existing Master ID, i.e., a person they know. If not, a new Master ID is created and becomes part of the matching game.

Some of you may wonder: “Is it using deterministic or probabilistic methods?” The answer is: both.

First, it will try to determine if there is a literal match – that is, if the device identifier is already in the database. If so, there is a direct match that we call deterministic. If not, we need Plan B: probabilistic matching. Plan B uses some fancy math (which I will sketch out below) to figure out if the various Attributes it’s seeing are similar enough to Attributes in the Master ID database to make it PROBABLE we have a match.

If not, rinse and repeat: New Master ID, attached to these Attributes, and associated with the device ID.

Simplified Version of Match Process

Simplified Version of Match Process

This is all somewhat less mystical than it sounds. I will take a moment here to list the specific pieces of information that Adelphic’s patent mentions. It’s interesting because it shows how sparse info can get in ad tech.

Deterministic matches are commonly made by finding one-to-one identity with:

  • Cookie – any unique browser cookie set by the advertiser or its agents, like a DSP
  • Phone number
  • Email address
  • UserID – explicit identifier that is similar to a cookie, known only by advertiser or its agents
  • Device ID – some of the ones listed in the patent don’t exist anymore (like Apple’s beloved UDID and various open standards that didn’t take); practically, this is limited to ADID and IDFA available only to app developers for Android and IoS

These identifiers are often sent with the HTTP request as a query parameter. This means something that looks like a URL is sent to the matcher’s server and it contains a string after the matcher’s address, like:;

The matcher has set up an API that can be pinged with this request. It sends back a match (if it exists) and other relevant info (ditto). Of course, many times a marketer will not have an email, phone number or even known cookie. In practice, this kind of deterministic identity is useful for people who have explicitly given us information – usually because they are a customer or prospect – and this is stored either in their browser (e.g., cookie) or on the web page itself (e.g., email).

Most people will not have a deterministic identifier attached to their API request. So we go to probabilistic matching. Here the matcher will use any and all information it can find. Some of it is simply the kind of information that is always sent back and forth when machines on the internet communicate. This information is contained in the IAB specs and is routinely part of any exchange of data on the wires.

It comes in two flavors: device and system data; and so-called behavioral data.

Device and system data spelled out by the patent include:

  • OS type and version
  • Device brand and model
  • Clock setting
  • Time zone
  • Speed
  • Language default

These seem quite mundane but contain more information than we think. OS versions can get very specific. While default language = english doesn’t say much, I’ve heard that clock settings down to multiple decimal points can be quite revealing.

Then there is so-called behavioral data. (I say “so-called” because it isn’t always about what we humans call behaviors.)

Attribute data of this type are:

  • HTTP header – this is hidden text sent with the HTTP request and includes things like the date and time, characters accepted, various settings
  • User agent – the browser and version
  • App launch time
  • Page load time
  • Referrer – previous page visited is usually stored in the browser
  • Plug-ins used
  • Geography (latititude / longitude)
  • URL – specific page person is on; this can also be visited to determine the type of content being viewed (more on this below)
  • Typing frequency
  • Gesture – these last two apply to apps, if you can capture some patterns in the person’s interaction with the app; I have no idea how often this is used and can’t find any real information about it, other than the obvious fact that people can have different typing frequencies and gestures. (If you know, DM me at @martykihn or comment below.)

This does not seem like a whole lot of information to describe a unique person, and it isn’t. But combined, it can be helpful. For example, if I open a browser and visit a Bernese mountain dog site and my referrer is a Crossfit gym in Bedford Hills, NY – well, that’s pretty unique. If I did the same thing on my iPad last week – which, to be honest, I probably did – there exists a MasterID with those behavioral Attributes and (assuming there are not thousands of people who do exactly the same thing, which there are NOT) the matcher can call us a likely match. Based purely on a URL and a Referrer.

To view the article in its entirety, visit Gartner.


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